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Stroke is a leading cause of epilepsy, especially in older adults. The SeLECT score remains the standard among post-stroke epilepsy (PSE) prediction tools. However, its broader validation is limited by the need to manually extract neuroimaging predictors (cortical and middle cerebral artery [MCA] involvement). Unlike the CAVE score, SeLECT did not evaluate acute stroke volume, which can now be quantified automatically. We aimed to determine whether stroke volume independently predicts PSE and compare its predictive contribution to SeLECT's neuroimaging variables.

SeLECT variables were manually extracted. Diffusion-weighted imaging volume was quantified using a validated convolutional neural network. Cox proportional hazards models for time to PSE were built by adding stroke volume (per 10 mL) and then removing cortical and/or MCA involvement. For each model, we analyzed variable significance, discrimination, and calibration.

Among 221 patients, 35 (15.8%) developed PSE. In our cohort, the original SeLECT score and the refit model had a C-index of 0.669 and 0.642, respectively. Adding stroke volume resulted in a C-index of 0.656. Retaining volume while removing cortical and MCA involvement resulted in C-indices of 0.664 and 0.668, respectively. Keeping stroke volume and removing both variables increased the C-index to 0.679. Calibration was good for all models. Stroke volume in crease by 10 mL was an independent predictor of 12% increased PSE risk across all models.

Acute stroke volume is an independent PSE predictor. Stroke volume offered comparable discrimination to the neuroimaging components of the SeLECT score, supporting its use as a scalable and automated alternative.